Key Themes:

The framework addresses two main themes: mitigating bias in data sources and building community trust. Purves highlights the importance of careful selection of data sources, noting that a focus solely on crime data, especially arrest data, can still perpetuate biased outcomes. Jenkins emphasizes the framework’s goal to help police integrate algorithmic patrol management responsibly, fostering community trust in the process.

Transparency and Accountability:

Law enforcement agencies often do not disclose their use of algorithms for predicting crime. To build public trust, the framework recommends transparency in the development process, inclusion of ethical requirements in product specifications, and continuous evaluation of metrics related to bias, transparency, and explainability. The goal is to demystify these technologies and guide public conversations surrounding their use.

Implementation Challenges and Opportunities:

Implementing the framework involves a range of recommendations, from prioritizing transparency in development to hiring a chief ethics officer. While some recommendations are straightforward, others may pose financial challenges. However, the framework suggests that doing the right thing is often good business, with some tech companies already taking a stand on certain applications of their systems.

Looking Ahead

Researchers are actively engaging with a major developer of data-driven police technologies to test the framework’s recommendations. The aspiration is to contribute to positive outcomes for public safety and police-community relations, moving towards a more harmonious future. As the ethical landscape of data-driven policing evolves, this framework stands as a significant step in fostering responsible development and deployment of technology for the benefit of all.